The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference
- Submitting institution
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University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22981_49358
- Type
- D - Journal article
- DOI
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10.1016/j.jneumeth.2013.10.018
- Title of journal
- Journal of Neuroscience Methods
- Article number
- -
- First page
- 50
- Volume
- 223
- Issue
- -
- ISSN
- 0165-0270
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- URL
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https://doi.org/10.1016/j.jneumeth.2013.10.018
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 329
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes an open-access open-source software toolbox, which implements a state-of-the-art operationalisation of Granger causality, an increasingly widespread statistical method for assessing directed functional connectivity in neural and other complex dynamical systems. It has become the academic standard for such analyses internationally, with field-weighted citation impact 8.39 (Scopus). Notably, more than 60 published studies in a variety of fields, including the neurosciences, psychiatry, econometrics and the geosciences, have used the toolbox software as part of their methods. These include studies published in Nature (9 papers, including Nature Neuroscience), the top-tier journal Neuron (9 papers), and PNAS (4 papers).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -